[Bugs] Fix Docs Build Problem (#97)
* [Bugs] Docs fixed * Update contributing.md * Update index.md * fix lua to text * fix title size
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## Operator accuracy test
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# Operator accuracy test
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### torch_xray
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## torch_xray
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torch_xray is an operator precision analysis tool that can dump module-level input-output precision comparisons and automatically construct operator unit tests.
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#### 1.Download and install
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### 1.Download and install
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***\*python3.10:\****
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@@ -20,9 +20,9 @@ bos:/klx-sdk-release-public/xpytorch/dev_kl3/torch_xray/latest/torch_xray-999.9.
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Note that the same installation package must be used when using it in different environments.
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#### 2.Use
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### 2.Use
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##### Dump module-level inputs and outputs and compare their precision.
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#### Dump module-level inputs and outputs and compare their precision.
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Below is a sample code snippet used to dump the input and output of the vision module and compare the errors in the vllm framework.
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@@ -50,7 +50,7 @@ The results directory will generate an h5 file and a csv file.
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-rw-r--r-- 1 root root 71 Oct 31 13:11 globalrank-0_localrank-0_summary.csv
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```
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##### Data processing
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#### Data processing
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```bash
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summary xxx.h5 sum.txt
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@@ -91,7 +91,7 @@ The generated h5 file is processed using the summary command to generate a txt f
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+-------+------+------+-----------------------------------------------------------+-------------+-------------+--------------+-------------+
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```
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##### Accuracy Comparison
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#### Accuracy Comparison
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```bash
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# The results are stored in result.csv
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@@ -103,7 +103,7 @@ The `compare` command is used to process the H5 files generated on the GPU and X
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If you encounter a "no matched keys" problem, please refer to the instructions at the end of this article for a solution.
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##### Example of results
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#### Example of results
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```bash
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+-------+--------+-----------------------------------------------------------+--------+-----------+-------------+-------------+--------+
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@@ -141,11 +141,11 @@ If you encounter a "no matched keys" problem, please refer to the instructions a
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Generally, the main focus is on Min Err/Max Err.
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##### Indicator Explanation
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#### Indicator Explanation
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To be improved...
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#### The dump operator is tested and run.
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### The dump operator is tested and run.
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```bash
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X_DEBUG=0x102 # trace operator name、arguments shape、dtype、data_range
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@@ -199,13 +199,13 @@ This is the file directory.
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│ ├── dump.json # Information needed to generate unit tests, such as input/output size and dtype.
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```
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##### Generate unit test
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#### Generate unit test
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jprof --cpu_init --blacklist --factory=load dump.json
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Create a pytests directory in the current directory to store unit tests.
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##### Run unit test
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#### Run unit test
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The GPU only needs to copy the XPU's pytests directory and execute it.
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@@ -216,14 +216,14 @@ Since the unit test program defaults to finding the actual dumped tensors using
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pytest --detail_compare_path=./xxx.csv proc_xxx/pytests/ --seed 42
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```
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##### Results Comparison
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#### Results Comparison
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```bash
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# After obtaining two result CSV files, compare them and generate result.csv.
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summary_diff_check ./xpu.csv ./gpu.csv ./result.csv
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```
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##### Example of results
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#### Example of results
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```bash
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+------------+-----------------------+-------------+-------------+-----------+----------+---------+---------+----------+
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@@ -242,9 +242,9 @@ summary_diff_check ./xpu.csv ./gpu.csv ./result.csv
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The main focus is on the values of gpu_1e-1, xpu_1e-1, etc., which represent the number of elements whose error between the gpu/xpu result and the cpu result exceeds the order of 1e-n. This serves as the primary basis for determining whether there is a problem with the operator's precision.
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#### Replenish
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### Replenish
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##### Bypassing the issue of differing naming conventions between Kunlun Card and GPU modules, which prevents diff calculation.
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#### Bypassing the issue of differing naming conventions between Kunlun Card and GPU modules, which prevents diff calculation.
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```bash
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#
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